| # vgg11 | |
| Implementation of VGG proposed in [Very Deep Convolutional Networks For | |
| Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556.pdf) | |
| ``` python | |
| VGG.vgg11() | |
| VGG.vgg13() | |
| VGG.vgg16() | |
| VGG.vgg19() | |
| VGG.vgg11_bn() | |
| VGG.vgg13_bn() | |
| VGG.vgg16_bn() | |
| VGG.vgg19_bn() | |
| ``` | |
| Please be aware that the [bn]{.title-ref} models uses BatchNorm but | |
| they are very old and people back then don\'t know the bias is | |
| superfluous in a conv followed by a batchnorm. | |
| Examples: | |
| ``` python | |
| # change activation | |
| VGG.vgg11(activation = nn.SELU) | |
| # change number of classes (default is 1000 ) | |
| VGG.vgg11(n_classes=100) | |
| # pass a different block | |
| from nn.models.classification.senet import SENetBasicBlock | |
| VGG.vgg11(block=SENetBasicBlock) | |
| # store the features tensor after every block | |
| ``` | |